This book introduces resource-aware data fusion algorithms that generate inferences by combining data from multiple sources-techniques useful in centralized and distributed systems to overcome sensor failure, technological limitations, and coverage problems.

This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative.

• Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise;
• Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed;
• Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines.

Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines Includes supplementary material: sn.pub/extras

Inhalt
Introduction to wireless sensor networks.- Data fusion in wireless sensor networks.- Centralized data fusion algorithms.- Introduction to Kalman filters.- Proposed distributed Kalman filter.- Simulation and experimental results.